Administering a lock for resources in a distributed computing environment

ABSTRACT

In a distributed computing environment that includes compute nodes, where the compute nodes execute a plurality of tasks, a lock for resources may be administered. Administering the lock may be carried out by requesting, in an atomic operation by a requesting task, the lock, including: determining, by the requesting task, whether the lock is available; if the lock is available, obtaining the lock; and if the lock is unavailable, joining, by the requesting task, a queue of tasks waiting for availability of the lock.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation application of and claims priorityfrom U.S. patent application Ser. No. 14/,148,176, filed on Jan. 6,2014.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The field of the invention is data processing, or, more specifically,methods, apparatus, and products for administering a lock for resourcesin a distributed computing environment.

2. Description Of Related Art

The development of the EDVAC computer system of 1948 is often cited asthe beginning of the computer era. Since that time, computer systemshave evolved into extremely complicated devices. Today's computers aremuch more sophisticated than early systems such as the EDVAC. Computersystems typically include a combination of hardware and softwarecomponents, application programs, operating systems, processors, buses,memory, input/output devices, and so on. As advances in semiconductorprocessing and computer architecture push the performance of thecomputer higher and higher, more sophisticated computer software hasevolved to take advantage of the higher performance of the hardware,resulting in computer systems today that are much more powerful thanjust a few years ago.

Computer systems today may be employed in distributed computingenvironments, such as massively parallel computers, in which manycomputer systems are coupled for data communications and utilizedtogether for additional computational bandwidth. In such a distributedcomputing environment, tasks of the computer system may share resources,such as memory, input/output (I/O), and the like. Controlling theavailability and use of the shared resources among many tasks is oftencarried out through use of a lock. Locks, today, however, areadministered in such a way that can result in a task chasing the lockforever without ever (or at least for an appreciable amount of time)being able to obtain the lock. That is, locks in today's distributedcomputing environment are often implemented in an unfair manner betweentasks in the environment.

SUMMARY OF THE INVENTION

Methods, apparatus, and products for administering a lock for resourcesin a distributed computing environment are disclosed in thisspecification. The distributed computing environment includes aplurality of compute nodes, with the compute nodes executing a pluralityof tasks. In such an environment, a lock may be administered by:requesting, in an atomic operation by a requesting task, a lock,including: determining, by the requesting task, whether the lock isavailable; if the lock is available, obtaining the lock; and if the lockis unavailable, joining, by the requesting task, a queue of taskswaiting for availability of the lock.

The foregoing and other objects, features and advantages of theinvention will be apparent from the following more particulardescriptions of exemplary embodiments of the invention as illustrated inthe accompanying drawings wherein like reference numbers generallyrepresent like parts of exemplary embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary system for administering a lock forresources in a distributed computing environment according toembodiments of the present invention.

FIG. 2 sets forth a block diagram of an example compute node useful in aparallel computer capable of administering a lock for resources in adistributed computing environment according to embodiments of thepresent invention.

FIG. 3 sets forth a block diagram of an example Point-To-Point Adapteruseful in systems for administering a lock for resources in adistributed computing environment according to embodiments of thepresent invention.

FIG. 4 sets forth a block diagram of an example Global Combining Network

Adapter useful in systems for administering a lock for resources in adistributed computing environment according to embodiments of thepresent invention.

FIG. 5 sets forth a line drawing illustrating an example datacommunications network optimized for point-to-point operations useful insystems capable of administering a lock for resources in a distributedcomputing environment according to embodiments of the present invention.

FIG. 6 sets forth a line drawing illustrating an example globalcombining network useful in systems capable of administering a lock forresources in a distributed computing environment according toembodiments of the present invention.

FIG. 7 sets forth a flow chart illustrating an example method foradministering a lock for resources in a distributed computingenvironment according to embodiments of the present invention.

FIG. 8 sets forth a flow chart illustrating another example method foradministering a lock for resources in a distributed computingenvironment according to embodiments of the present invention.

FIG. 9 sets forth a flow chart illustrating another example method foradministering a lock for resources in a distributed computingenvironment according to embodiments of the present invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Exemplary methods, apparatus, and products for administering a lock forresources in a distributed computing environment in accordance with thepresent invention are described with reference to the accompanyingdrawings, beginning with FIG. 1. One example of a distributed computingenvironment for which a lock for resources may be administered inaccordance with embodiments of the present invention is a parallelcomputer. Parallel computing is the simultaneous execution of the sametask (split up and specially adapted) on multiple processors in order toobtain results faster. Parallel computing is based on the fact that theprocess of solving a problem usually can be divided into smaller tasks,which may be carried out simultaneously with some coordination.

Parallel computers execute parallel algorithms. A parallel algorithm canbe split up to be executed a piece at a time on many differentprocessing devices, and then put back together again at the end to get adata processing result. Some algorithms are easy to divide up intopieces. Splitting up the job of checking all of the numbers from one toa hundred thousand to see which are primes could be done, for example,by assigning a subset of the numbers to each available processor, andthen putting the list of positive results back together. In thisspecification, the multiple processing devices that execute theindividual pieces of a parallel program are referred to as ‘computenodes.’ A parallel computer is composed of compute nodes and otherprocessing nodes as well, including, for example, input/output (‘I/O’)nodes, and service nodes.

Parallel algorithms are valuable because it is faster to perform somekinds of large computing tasks via a parallel algorithm than it is via aserial (non-parallel) algorithm, because of the way modern processorswork. It is far more difficult to construct a computer with a singlefast processor than one with many slow processors with the samethroughput. There are also certain theoretical limits to the potentialspeed of serial processors. On the other hand, every parallel algorithmhas a serial part and so parallel algorithms have a saturation point.After that point adding more processors does not yield any morethroughput but only increases the overhead and cost.

Parallel algorithms are designed also to optimize one more resource thedata communications requirements among the nodes of a parallel computer.There are two ways parallel processors communicate, shared memory ormessage passing. Shared memory processing needs additional locking forthe data and imposes the overhead of additional processor and bus cyclesand also serializes some portion of the algorithm.

Message passing processing uses high-speed data communications networksand message buffers, but this communication adds transfer overhead onthe data communications networks as well as additional memory need formessage buffers and latency in the data communications among nodes.Designs of parallel computers use specially designed data communicationslinks so that the communication overhead will be small but it is theparallel algorithm that decides the volume of the traffic.

Many data communications network architectures are used for messagepassing among nodes in parallel computers. Compute nodes may beorganized in a network as a ‘torus’ or ‘mesh,’ for example. Also,compute nodes may be organized in a network as a tree. A torus networkconnects the nodes in a three-dimensional mesh with wrap around links.Every node is connected to its six neighbors through this torus network,and each node is addressed by its x,y,z coordinate in the mesh. In sucha manner, a torus network lends itself to point to point operations. Ina tree network, the nodes typically are connected into a binary tree:each node has a parent, and two children (although some nodes may onlyhave zero children or one child, depending on the hardwareconfiguration). Although a tree network typically is inefficient inpoint to point communication, a tree network does provide high bandwidthand low latency for certain collective operations, message passingoperations where all compute nodes participate simultaneously, such as,for example, an allgather operation. In computers that use a torus and atree network, the two networks typically are implemented independentlyof one another, with separate routing circuits, separate physical links,and separate message buffers.

In some parallel computers, each compute node may execute one or moretasks—a process of execution for a parallel application. Each task mayinclude a number of endpoints. Each endpoint is a data communicationsendpoint that supports communications among many other endpoints andtasks. In this way, endpoints support collective operations in aparallel computer by supporting the underlying message passingresponsibilities carried out during a collective operation. In someparallel computers, each compute node may execute a single taskincluding a single endpoint. For example, a parallel computer thatoperates with the Message Passing Interface (‘MPI’) described below inmore detail may execute a single rank on each compute node of theparallel computer. In such implementations, the terms task, endpoint,and rank are effectively synonymous.

FIG. 1 illustrates an exemplary system for administering a lock forresources in a distributed computing environment according toembodiments of the present invention. The system of FIG. 1 includes aparallel computer (100), non-volatile memory for the computer in theform of a data storage device (118), an output device for the computerin the form of a printer (120), and an input/output device for thecomputer in the form of a computer terminal (122).

The parallel computer (100) in the example of FIG. 1 includes aplurality of compute nodes (102). The compute nodes (102) are coupledfor data communications by several independent data communicationsnetworks including a high speed Ethernet network (174), a Joint TestAction Group (‘JTAG’) network (104), a global combining network (106)which is optimized for collective operations using a binary tree networktopology, and a point-to-point network (108), which is optimized forpoint-to-point operations using a torus network topology. The globalcombining network (106) is a data communications network that includesdata communications links connected to the compute nodes (102) so as toorganize the compute nodes (102) as a binary tree. Each datacommunications network is implemented with data communications linksamong the compute nodes (102). The data communications links providedata communications for parallel operations among the compute nodes(102) of the parallel computer (100).

The compute nodes (102) of the parallel computer (100) are organizedinto at least one operational group (132) of compute nodes forcollective parallel operations on the parallel computer (100). Eachoperational group (132) of compute nodes is the set of compute nodesupon which a collective parallel operation executes. Each compute nodein the operational group (132) is assigned a unique rank that identifiesthe particular compute node in the operational group (132). Collectiveoperations are implemented with data communications among the computenodes of an operational group. Collective operations are those functionsthat involve all the compute nodes of an operational group (132). Acollective operation is an operation, a message-passing computer programinstruction that is executed simultaneously, that is, at approximatelythe same time, by all the compute nodes in an operational group (132) ofcompute nodes. Such an operational group (132) may include all thecompute nodes (102) in a parallel computer (100) or a subset all thecompute nodes (102). Collective operations are often built aroundpoint-to-point operations. A collective operation requires that allprocesses on all compute nodes within an operational group (132) callthe same collective operation with matching arguments. A ‘broadcast’ isan example of a collective operation for moving data among compute nodesof an operational group. A ‘reduce’ operation is an example of acollective operation that executes arithmetic or logical functions ondata distributed among the compute nodes of an operational group (132).An operational group (132) may be implemented as, for example, an MPI‘communicator.’

‘MPI’ refers to ‘Message Passing Interface,’ a prior art parallelcommunications library, a module of computer program instructions fordata communications on parallel computers. Examples of prior-artparallel communications libraries that may be improved for use insystems configured according to embodiments of the present inventioninclude MPI and the ‘Parallel Virtual Machine’ (‘PVM’) library. PVM wasdeveloped by the University of Tennessee, The Oak Ridge NationalLaboratory and Emory University. MPI is promulgated by the MPI Forum, anopen group with representatives from many organizations that define andmaintain the MPI standard. MPI at the time of this writing is a de factostandard for communication among compute nodes running a parallelprogram on a distributed memory parallel computer. This specificationsometimes uses MPI terminology for ease of explanation, although the useof MPI as such is not a requirement or limitation of the presentinvention.

Some collective operations have a single originating or receivingprocess running on a particular compute node in an operational group(132). For example, in a ‘broadcast’ collective operation, the processon the compute node that distributes the data to all the other computenodes is an originating process. In a ‘gather’ operation, for example,the process on the compute node that received all the data from theother compute nodes is a receiving process. The compute node on whichsuch an originating or receiving process runs is referred to as alogical root.

Most collective operations are variations or combinations of four basicoperations: broadcast, gather, scatter, and reduce. The interfaces forthese collective operations are defined in the MPI standards promulgatedby the MPI Forum. Algorithms for executing collective operations,however, are not defined in the MPI standards. In a broadcast operation,all processes specify the same root process, whose buffer contents willbe sent. Processes other than the root specify receive buffers. Afterthe operation, all buffers contain the message from the root process.

A scatter operation, like the broadcast operation, is also a one-to-manycollective operation. In a scatter operation, the logical root dividesdata on the root into segments and distributes a different segment toeach compute node in the operational group (132). In scatter operation,all processes typically specify the same receive count. The sendarguments are only significant to the root process, whose bufferactually contains sendcount*N elements of a given datatype, where N isthe number of processes in the given group of compute nodes. The sendbuffer is divided and dispersed to all processes (including the processon the logical root). Each compute node is assigned a sequentialidentifier termed a ‘rank.’ After the operation, the root has sentsendcount data elements to each process in increasing rank order. Rank 0receives the first sendcount data elements from the send buffer. Rank 1receives the second sendcount data elements from the send buffer, and soon.

A gather operation is a many-to-one collective operation that is acomplete reverse of the description of the scatter operation. That is, agather is a many-to-one collective operation in which elements of adatatype are gathered from the ranked compute nodes into a receivebuffer in a root node.

A reduction operation is also a many-to-one collective operation thatincludes an arithmetic or logical function performed on two dataelements. All processes specify the same ‘count’ and the same arithmeticor logical function. After the reduction, all processes have sent countdata elements from compute node send buffers to the root process. In areduction operation, data elements from corresponding send bufferlocations are combined pair-wise by arithmetic or logical operations toyield a single corresponding element in the root process' receivebuffer. Application specific reduction operations can be defined atruntime. Parallel communications libraries may support predefinedoperations. MPI, for example, provides the following pre-definedreduction operations:

-   -   MPI_MAX maximum    -   MPI_MIN minimum    -   MPI_SUM sum    -   MPI_PROD product    -   MPI_LAND logical and    -   MPI_BAND bitwise and    -   MPI_LOR logical or    -   MPI_BOR bitwise or    -   MPI_LXOR logical exclusive or    -   MPI_BXOR bitwise exclusive or

In addition to compute nodes, the parallel computer (100) includesinput/output (‘I/O’) nodes (110, 114) coupled to compute nodes (102)through the global combining network (106). The compute nodes (102) inthe parallel computer (100) may be partitioned into processing sets suchthat each compute node in a processing set is connected for datacommunications to the same I/O node. Each processing set, therefore, iscomposed of one I/O node and a subset of compute nodes (102). The ratiobetween the number of compute nodes to the number of I/O nodes in theentire system typically depends on the hardware configuration for theparallel computer (102). For example, in some configurations, eachprocessing set may be composed of eight compute nodes and one I/O node.In some other configurations, each processing set may be composed ofsixty-four compute nodes and one I/O node. Such example are forexplanation only, however, and not for limitation. Each I/O nodeprovides I/O services between compute nodes (102) of its processing setand a set of I/O devices. In the example of FIG. 1, the I/O nodes (110,114) are connected for data communications I/O devices (118, 120, 122)through local area network (‘LAN’) (130) implemented using high-speedEthernet.

The parallel computer (100) of FIG. 1 also includes a service node (116)coupled to the compute nodes through one of the networks (104). Servicenode (116) provides services common to pluralities of compute nodes,administering the configuration of compute nodes, loading programs intothe compute nodes, starting program execution on the compute nodes,retrieving results of program operations on the compute nodes, and soon. Service node (116) runs a service application (124) and communicateswith users (128) through a service application interface (126) that runson computer terminal (122).

The parallel computer (100) of FIG. 1 operates generally foradministering a lock for resources in a distributed computingenvironment in accordance with embodiments of the present invention. Alock may be utilized to control access to a variety of resources in theparallel computer of FIG. 1. Examples of such resources may includenetwork resources, storage resources, memory resources, I/O resources,processing resources, co-processing resources, software resources, andthe like.

In the example of FIG. 1, each of the compute nodes (102) may executeone or more tasks and one of the tasks—referred to here as a requestingtask—may request, in an atomic operation, a lock. An atomic operation isan operation that will always be executed without any other processbeing able to read or change state that is read or changed during theoperation. Atomic operations can operate to reduce or eliminate raceconditions. The atomic operation, in this example, enables a requestingtask to attempt to obtain a lock, without another task interrupting thatattempt. As part of the atomic operation, the requesting tasks maydetermine whether the lock is available and if the lock is available,obtaining the lock. If the lock is unavailable, the requesting taskjoins a queue of tasks waiting for availability of the lock.

Administering a lock for resources in a distributed computingenvironment according to embodiments of the present invention isgenerally implemented on a parallel computer that includes a pluralityof compute nodes organized for collective operations through at leastone data communications network. In fact, such computers may includethousands of such compute nodes. Each compute node is in turn itself akind of computer composed of one or more computer processing cores, itsown computer memory, and its own input/output adapters. For furtherexplanation, therefore, FIG. 2 sets forth a block diagram of an examplecompute node (102) useful in a parallel computer capable ofadministering a lock for resources in a distributed computingenvironment according to embodiments of the present invention. Thecompute node (102) of FIG. 2 includes a plurality of processing cores(165) as well as RAM (156). The processing cores (165) of FIG. 2 may beconfigured on one or more integrated circuit dies. Processing cores(165) are connected to RAM (156) through a high-speed memory bus (155)and through a bus adapter (194) and an extension bus (168) to othercomponents of the compute node. Stored in RAM (156) is an applicationprogram (159), a module of computer program instructions that carriesout parallel, user-level data processing using parallel algorithms.

Also stored RAM (156) is a parallel communications library (161), alibrary of computer program instructions that carry out parallelcommunications among compute nodes, including point-to-point operationsas well as collective operations. A library of parallel communicationsroutines may be developed from scratch for use in systems according toembodiments of the present invention, using a traditional programminglanguage such as the C programming language, and using traditionalprogramming methods to write parallel communications routines that sendand receive data among nodes on two independent data communicationsnetworks. Alternatively, existing prior art libraries may be improved tooperate according to embodiments of the present invention. Examples ofprior-art parallel communications libraries include the ‘Message PassingInterface’ (‘MPI’) library and the ‘Parallel Virtual Machine’ (‘PVM’)library.

Also stored in RAM (156) are a number of tasks (226) executing on thecompute node (102). A task may be a process, thread of execution, orother instantiation of a parallel application or operating systemexecuted in the parallel computer. A method of administering a lock forresources in a distributed computing environment that includes thecompute node (102) of FIG. 2 which executes a number of tasks (226),includes: requesting, in an atomic operation by a requesting task, alock (228). The requesting task may be one of the tasks (226) executingon the compute node (102) of FIG. 2. As part of the atomic operation,the requesting task may determine, by the requesting task, whether thelock is available. If the lock is available, the requesting task obtainsthe lock and, if the lock is unavailable, the requesting task joins aqueue (230) of tasks waiting for availability of the lock.

The atomic operations described above may be effected with one or moreRDMA (Remote Direct Memory Access) operations. To that end, the computenode in the example of FIG. 2 includes a DMA controller (195) and a DMAengine (197). The DMA controller (195) is a module of logic circuitryconfigured to accept DMA instructions and operate a DMA engine (197) tocarry them out. The DMA engine (197) is a module of automated computingmachinery that implements, through communications with other DMA engineson other compute nodes, direct memory access to and from memory on itsown compute node and memory on other the compute nodes. Direct memoryaccess is a way of reading and writing to memory of compute nodes withreduced operational burden on the central processing units (165). A CPU(165) initiates a DMA transfer, but the CPU does not execute the DMAtransfer. A DMA transfer essentially copies a block of memory from onecompute node to another, from an origin to a target for a PUT operation,from a target to an origin for a GET operation.

Also stored in RAM (156) is an operating system (162), a module ofcomputer program instructions and routines for an application program'saccess to other resources of the compute node. It is typical for anapplication program and parallel communications library in a computenode of a parallel computer to run a single thread of execution with nouser login and no security issues because the thread is entitled tocomplete access to all resources of the node. The quantity andcomplexity of tasks to be performed by an operating system on a computenode in a parallel computer therefore are smaller and less complex thanthose of an operating system on a serial computer with many threadsrunning simultaneously. In addition, there is no video I/O on thecompute node (102) of FIG. 2, another factor that decreases the demandson the operating system. The operating system (162) may therefore bequite lightweight by comparison with operating systems of generalpurpose computers, a pared down version as it were, or an operatingsystem developed specifically for operations on a particular parallelcomputer. Operating systems that may usefully be improved, simplified,for use in a compute node include UNIX™, Linux™, Windows XP™, AIX™,IBM's i5/OS™, and others as will occur to those of skill in the art.

The example compute node (102) of FIG. 2 includes several communicationsadapters (172, 176, 180, 188) for implementing data communications withother nodes of a parallel computer. Such data communications may becarried out serially through RS-232 connections, through external busessuch as USB, through data communications networks such as IP networks,and in other ways as will occur to those of skill in the art.Communications adapters implement the hardware level of datacommunications through which one computer sends data communications toanother computer, directly or through a network. Examples ofcommunications adapters useful in apparatus useful for administering alock for resources in a distributed computing environment include modemsfor wired communications, Ethernet (IEEE 802.3) adapters for wirednetwork communications, and 802.11b adapters for wireless networkcommunications.

The data communications adapters in the example of FIG. 2 include aGigabit Ethernet adapter (172) that couples example compute node (102)for data communications to a Gigabit Ethernet (174). Gigabit Ethernet isa network transmission standard, defined in the IEEE 802.3 standard,that provides a data rate of 1 billion bits per second (one gigabit).Gigabit Ethernet is a variant of Ethernet that operates over multimodefiber optic cable, single mode fiber optic cable, or unshielded twistedpair.

The data communications adapters in the example of FIG. 2 include a JTAGSlave circuit (176) that couples example compute node (102) for datacommunications to a JTAG Master circuit (178). JTAG is the usual nameused for the IEEE 1149.1 standard entitled Standard Test Access Port andBoundary-Scan Architecture for test access ports used for testingprinted circuit boards using boundary scan. JTAG is so widely adaptedthat, at this time, boundary scan is more or less synonymous with JTAG.JTAG is used not only for printed circuit boards, but also forconducting boundary scans of integrated circuits, and is also useful asa mechanism for debugging embedded systems, providing a convenientalternative access point into the system. The example compute node ofFIG. 2 may be all three of these: It typically includes one or moreintegrated circuits installed on a printed circuit board and may beimplemented as an embedded system having its own processing core, itsown memory, and its own I/O capability. JTAG boundary scans through JTAGSlave (176) may efficiently configure processing core registers andmemory in compute node (102).

The data communications adapters in the example of FIG. 2 include aPoint-To-Point Network Adapter (180) that couples example compute node(102) for data communications to a network (108) that is optimal forpoint-to-point message passing operations such as, for example, anetwork configured as a three-dimensional torus or mesh. ThePoint-To-Point Adapter (180) provides data communications in sixdirections on three communications axes, x, y, and z, through sixbidirectional links: +x (181), −x (182), +y (183), −y (184), +z (185),and −z (186).

The data communications adapters in the example of FIG. 2 include aGlobal Combining Network Adapter (188) that couples example compute node(102) for data communications to a global combining network (106) thatis optimal for collective message passing operations such as, forexample, a network configured as a binary tree. The Global CombiningNetwork Adapter (188) provides data communications through threebidirectional links for each global combining network (106) that theGlobal Combining Network Adapter (188) supports. In the example of FIG.2, the Global Combining Network Adapter (188) provides datacommunications through three bidirectional links for global combiningnetwork (106): two to children nodes (190) and one to a parent node(192).

The example compute node (102) includes multiple arithmetic logic units(‘ALUs’). Each processing core (165) includes an ALU (166), and aseparate ALU (170) is dedicated to the exclusive use of the GlobalCombining Network Adapter (188) for use in performing the arithmetic andlogical functions of reduction operations, including an allreduceoperation. Computer program instructions of a reduction routine in aparallel communications library (161) may latch an instruction for anarithmetic or logical function into an instruction register (169). Whenthe arithmetic or logical function of a reduction operation is a ‘sum’or a ‘logical OR,’ for example, the collective operations adapter (188)may execute the arithmetic or logical operation by use of the ALU (166)in the processing core (165) or, typically much faster, by use of thededicated ALU (170) using data provided by the nodes (190, 192) on theglobal combining network (106) and data provided by processing cores(165) on the compute node (102).

Often when performing arithmetic operations in the global combiningnetwork adapter (188), however, the global combining network adapter(188) only serves to combine data received from the children nodes (190)and pass the result up the network (106) to the parent node (192).Similarly, the global combining network adapter (188) may only serve totransmit data received from the parent node (192) and pass the data downthe network (106) to the children nodes (190). That is, none of theprocessing cores (165) on the compute node (102) contribute data thatalters the output of ALU (170), which is then passed up or down theglobal combining network (106). Because the ALU (170) typically does notoutput any data onto the network (106) until the ALU (170) receivesinput from one of the processing cores (165), a processing core (165)may inject the identity element into the dedicated ALU (170) for theparticular arithmetic operation being perform in the ALU (170) in orderto prevent alteration of the output of the ALU (170). Injecting theidentity element into the ALU, however, often consumes numerousprocessing cycles. To further enhance performance in such cases, theexample compute node (102) includes dedicated hardware (171) forinjecting identity elements into the ALU (170) to reduce the amount ofprocessing core resources required to prevent alteration of the ALUoutput.

The dedicated hardware (171) injects an identity element thatcorresponds to the particular arithmetic operation performed by the ALU.For example, when the global combining network adapter (188) performs abitwise OR on the data received from the children nodes (190), dedicatedhardware (171) may inject zeros into the ALU (170) to improveperformance throughout the global combining network (106).

For further explanation, FIG. 3 sets forth a block diagram of an examplePoint-To-Point Adapter (180) useful in systems for administering a lockfor resources in a distributed computing environment according toembodiments of the present invention. The Point-To-Point Adapter (180)is designed for use in a data communications network optimized forpoint-to-point operations, a network that organizes compute nodes in athree-dimensional torus or mesh. The Point-To-Point Adapter (180) in theexample of FIG. 3 provides data communication along an x-axis throughfour unidirectional data communications links, to and from the next nodein the −x direction (182) and to and from the next node in the +xdirection (181). The Point-To-Point Adapter (180) of FIG. 3 alsoprovides data communication along a y-axis through four unidirectionaldata communications links, to and from the next node in the −y direction(184) and to and from the next node in the +y direction (183). ThePoint-To-Point Adapter (180) of FIG. 3 also provides data communicationalong a z-axis through four unidirectional data communications links, toand from the next node in the -z direction (186) and to and from thenext node in the +z direction (185).

For further explanation, FIG. 4 sets forth a block diagram of an exampleGlobal Combining Network Adapter (188) useful in systems foradministering a lock for resources in a distributed computingenvironment according to embodiments of the present invention. TheGlobal Combining Network Adapter (188) is designed for use in a networkoptimized for collective operations, a network that organizes computenodes of a parallel computer in a binary tree. The Global CombiningNetwork Adapter (188) in the example of FIG. 4 provides datacommunication to and from children nodes of a global combining networkthrough four unidirectional data communications links (190), and alsoprovides data communication to and from a parent node of the globalcombining network through two unidirectional data communications links(192).

For further explanation, FIG. 5 sets forth a line drawing illustratingan example data communications network (108) optimized forpoint-to-point operations useful in systems capable of administering alock for resources in a distributed computing environment according toembodiments of the present invention. In the example of FIG. 5, dotsrepresent compute nodes (102) of a parallel computer, and the dottedlines between the dots represent data communications links (103) betweencompute nodes. The data communications links are implemented withpoint-to-point data communications adapters similar to the oneillustrated for example in FIG. 3, with data communications links onthree axis, x, y, and z, and to and fro in six directions +x (181), −x(182), +y (183), −y (184), +z (185), and −z (186). The links and computenodes are organized by this data communications network optimized forpoint-to-point operations into a three dimensional mesh (105). The mesh(105) has wrap-around links on each axis that connect the outermostcompute nodes in the mesh (105) on opposite sides of the mesh (105).These wrap-around links form a torus (107). Each compute node in thetorus has a location in the torus that is uniquely specified by a set ofx, y, z coordinates. Readers will note that the wrap-around links in they and z directions have been omitted for clarity, but are configured ina similar manner to the wrap-around link illustrated in the x direction.For clarity of explanation, the data communications network of FIG. 5 isillustrated with only 27 compute nodes, but readers will recognize thata data communications network optimized for point-to-point operationsfor use in administering a lock for resources in a distributed computingenvironment in accordance with embodiments of the present invention maycontain only a few compute nodes or may contain thousands of computenodes. For ease of explanation, the data communications network of FIG.5 is illustrated with only three dimensions, but readers will recognizethat a data communications network optimized for point-to-pointoperations for use in administering a lock for resources in adistributed computing environment in accordance with embodiments of thepresent invention may in facet be implemented in two dimensions, fourdimensions, five dimensions, and so on. Several supercomputers now usefive dimensional mesh or torus networks, including, for example, IBM'sBlue Gene Q™.

For further explanation, FIG. 6 sets forth a line drawing illustratingan example global combining network (106) useful in systems capable ofadministering a lock for resources in a distributed computingenvironment according to embodiments of the present invention. Theexample data communications network of FIG. 6 includes datacommunications links (103) connected to the compute nodes so as toorganize the compute nodes as a tree. In the example of FIG. 6, dotsrepresent compute nodes (102) of a parallel computer, and the dottedlines (103) between the dots represent data communications links betweencompute nodes. The data communications links are implemented with globalcombining network adapters similar to the one illustrated for example inFIG. 4, with each node typically providing data communications to andfrom two children nodes and data communications to and from a parentnode, with some exceptions. Nodes in the global combining network (106)may be characterized as a physical root node (202), branch nodes (204),and leaf nodes (206). The physical root (202) has two children but noparent and is so called because the physical root node (202) is the nodephysically configured at the top of the binary tree. The leaf nodes(206) each has a parent, but leaf nodes have no children. The branchnodes (204) each has both a parent and two children. The links andcompute nodes are thereby organized by this data communications networkoptimized for collective operations into a binary tree (106). Forclarity of explanation, the data communications network of FIG. 6 isillustrated with only 31 compute nodes, but readers will recognize thata global combining network (106) optimized for collective operations foruse in administering a lock for resources in a distributed computingenvironment in accordance with embodiments of the present invention maycontain only a few compute nodes or may contain thousands of computenodes.

In the example of FIG. 6, each node in the tree is assigned a unitidentifier referred to as a ‘rank’ (250). The rank actually identifies atask or process that is executing a parallel operation according toembodiments of the present invention. Using the rank to identify a nodeassumes that only one such task is executing on each node. To the extentthat more than one participating task executes on a single node, therank identifies the task as such rather than the node. A rank uniquelyidentifies a task's location in the tree network for use in bothpoint-to-point and collective operations in the tree network. The ranksin this example are assigned as integers beginning with 0 assigned tothe root tasks or root node (202), 1 assigned to the first node in thesecond layer of the tree, 2 assigned to the second node in the secondlayer of the tree, 3 assigned to the first node in the third layer ofthe tree, 4 assigned to the second node in the third layer of the tree,and so on. For ease of illustration, only the ranks of the first threelayers of the tree are shown here, but all compute nodes in the treenetwork are assigned a unique rank.

For further explanation, FIG. 7 sets forth a flow chart illustrating anexample method administering a lock for resources in a distributedcomputing environment according to embodiments of the present invention.The distributed computing environment in which the method of FIG. 7 iscarried out may be similar to the example parallel computer (100) ofFIG. 1 which includes a number of compute nodes (102, with each computenode executing a plurality of tasks.

The method of FIG. 7 includes selecting (700), for each of a pluralityof locks, a different one of the plurality of compute nodes to host thelock. That is, each lock may be hosted by a different compute node. Inthis way, the burden of hosting the locks is distributed amongst thecompute nodes, rather than being centralized in one compute node.Selecting (700) a compute node to host a lock may be carried out in avariety of ways including selecting a compute node in dependence upon aset of selection criteria. The selection criteria may specify an addressor other identifier of a compute node to be selected for each lock, mayspecify that only a compute node with a particular workload less than apredefined threshold be selected, that a compute node with particularcommunication abilities be selected, that a compute node with particularmemory availability be selected, that a compute node with some otherhardware or software capabilities be selected, and so on.

The method of FIG. 7 also includes requesting (702), in an atomicoperation by a requesting task, a lock (714). The lock may beimplemented as a memory location that includes either a predefined valuerepresenting the lock is available or an identifier of a task currentlyholding the lock.

In the example of FIG. 7, requesting (702) the lock is carried out bydetermining (704), by the requesting task, whether the lock isavailable. Determining (704) whether the lock is available is availablemay be carried out by determining whether a task identifier is stored inthe memory location of the lock or whether a predefined valuerepresenting availability of the lock is stored in the memory locationof the lock.

If the lock is available, the method of FIG. 7 continues by obtaining(708), by the requesting task, the lock (708). Obtaining (708) the lockmay be carried out by storing, in the memory location of the lock (714),the identifier of the requesting task.

If the lock is unavailable, the method of FIG. 7 continues by joining(706), by the requesting task, a queue (718) of tasks waiting foravailability of the lock (714). Joining (706) a queue (718) of taskswaiting for availability of the lock (714) may be carried out by storingin a next available element of the queue, the identifier of the requesttask.

The method of FIG. 7 also includes unlocking (710), in an atomicoperation by a lock holding task, the lock (714). In the method of FIG.7, unlocking (710) the lock (714) may be carried out by removing thelock holding task's identifier from the lock's (714) memory location andproviding (712) the lock to a next task in the queue (718) of taskswaiting for availability of the lock. Providing (712) the lock to a nexttask in the queue of tasks waiting for availability of the lock may becarried out by, replacing, in the memory location of the lock (714), thelock holding task's identifier with the identifier of the next task's inthe queue. Then, the lock hold tasks (having released the lock) maynotify the next tasks of the change.

FIG. 8 sets forth a flow chart illustrating another example method foradministering a lock for resources in a distributed computingenvironment according to embodiments of the present invention. Themethod of FIG. 8 is similar to the method of FIG. 7 in that the methodof FIG. 8 may also be carried out in a distributed computing environmentthat includes a number of compute nodes where each node executes one ormore tasks. The method of FIG. 8 is also similar to the method of FIG. 7in that the method of FIG. 8 also includes: requesting (702) a lock(714), including: determining (704) whether the lock is available;obtaining (708) the lock if the lock is available; and joining (706) aqueue (718) of tasks waiting for availability of the lock if the lock isunavailable.

The method of FIG. 8 differs from the method of FIG. 7, however, in thatdetermining (704) whether the lock is available includes comparing (802)a value in a memory location of the lock (714) to a predefined valuerepresenting an available lock, determining (804) that the lock (714) isavailable if the value in the memory location matches the predefinedvalue, and determining (806) that the lock is unavailable if the valuein the memory location does not match the lock.

In the method of FIG. 8, obtaining (708) the lock (714) includesswapping (808) the value in the memory location of the lock (714) withan identifier of the requesting task. Also in the method of FIG. 8,joining (706) a queue of tasks waiting for availability of the lock iscarried out by pushing (810) the identifier of the requesting task ontothe queue.

In the method of FIG. 8, the memory location of the lock (714) and thequeue may be an object allocated in memory space shared by the each oftasks. An example of an API that provides interfaces for applications tomanipulate and access shared memory space includes SHMEM™ API from SGI.When allocated in memory shared memory space, swapping (808) the valuein the memory location with the identifier of the requesting task andpushing (810) the identifier of the requesting task onto the queue maybe carried out by atomic RDMA (Remote Direct Memory Access) operations.That is, in embodiments of the present invention, RDMA may be utilizedto administer the lock (714) and queue (718), further reducing theburden on the compute node (102) hosting the lock (714).

FIG. 9 sets forth a flow chart illustrating another example method foradministering a lock for resources in a distributed computingenvironment according to embodiments of the present invention. Themethod of FIG. 9 is similar to the method of FIG. 7 in that the methodof FIG. 9 may also be carried out in a distributed computing environmentthat includes a number of compute nodes where each node executes one ormore tasks. The method of FIG. 9 is also similar to the method of FIG. 7in that the method of FIG. 9 also includes: requesting (702) a lock(714), including: determining (704) whether the lock is available;obtaining (708) the lock if the lock is available; and joining (706) aqueue (718) of tasks waiting for availability of the lock if the lock isunavailable.

The method of FIG. 9 differs from the method of FIG. 7, however, in thatin the method of FIG. 9, unlocking (710) the lock includes notifying(906) the next task of the availability of the lock. Notifying the nexttask may be carried out by the task unlocking the lock or by a DMAengine or controller through RDMA operations.

Also in the method of FIG. 9, providing (712) the lock to the next taskin the queue of tasks waiting for availability of the lock is carriedout by: popping (902) from the queue (702) an identifier of the nexttask in the queue and swapping (904) the identifier of the next taskwith an identifier of the lock holding task stored as a value in amemory location of the lock.

In the method of FIG. 9, the queue and memory location of the lock (714)may be objects allocated in memory shared by the plurality of tasks. Inthis way, popping (902) the identifier of the next task from the queueand swapping (904) the identifier of the next task with the identifierof the lock holding task stored as a value in a memory location of thelock may be carried out by one or more atomic RDMA operations.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readabletransmission medium or a computer readable storage medium. A computerreadable storage medium may be, for example, but not limited to, anelectronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer readable storage medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer readable storage medium may be any tangible medium that cancontain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer readable transmission medium may include a propagated datasignal with computer readable program code embodied therein, forexample, in baseband or as part of a carrier wave. Such a propagatedsignal may take any of a variety of forms, including, but not limitedto, electro-magnetic, optical, or any suitable combination thereof. Acomputer readable transmission medium may be any computer readablemedium that is not a computer readable storage medium and that cancommunicate, propagate, or transport a program for use by or inconnection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

It will be understood from the foregoing description that modificationsand changes may be made in various embodiments of the present inventionwithout departing from its true spirit. The descriptions in thisspecification are for purposes of illustration only and are not to beconstrued in a limiting sense. The scope of the present invention islimited only by the language of the following claims.

1. A method of administering a lock for resources in a distributedcomputing environment comprising plurality of compute nodes, the computenodes executing a plurality of tasks, the method comprising: requesting,in an atomic operation by a requesting task, a lock, including:determining whether the lock is available; if the lock is available,obtaining the lock; and if the lock is unavailable, joining a queue oftasks waiting for availability of the lock.
 2. The method of claim 1,wherein: determining whether the lock is available further comprisescomparing a value in a memory location of the lock to a predefined valuerepresenting an available lock, determining that the lock is availableif the value in the memory location matches the predefined value, anddetermining that the lock is unavailable if the value in the memorylocation does not match the lock; obtaining the lock further comprisesswapping the value in the memory location with an identifier of therequesting task; and joining a queue of tasks waiting for availabilityof the lock further comprises pushing the identifier of the requestingtask onto the queue.
 3. The method of claim 2, wherein swapping thevalue in the memory location with the identifier of the requesting taskand pushing the identifier of the requesting task onto the queue furthercomprises one or more atomic RDMA (Remote Direct Memory Access)operations.
 4. The method of claim 2, wherein each of the memorylocation of the lock and the queue comprises an object allocated inmemory space shared by the plurality of tasks.
 5. The method of claim 1,further comprising unlocking, in an atomic operation by a lock holdingtask, the lock, including providing the lock to a next task in the queueof tasks waiting for availability of the lock.
 6. The method of claim 5,wherein unlocking the lock further comprises notifying the next task ofthe availability of the lock.
 7. The method of claim 5, whereinproviding the lock to the next task in the queue of tasks waiting foravailability of the lock further comprises: popping from the queue anidentifier of the next task in the queue; and swapping the identifier ofthe next task with an identifier of the lock holding task stored as avalue in a memory location of the lock.
 8. The method of claim 7,wherein popping the identifier of the next task from the queue andswapping the identifier of the next task with the identifier of the lockholding task stored as a value in a memory location of the lock furthercomprises one or more atomic RDMA (Remote Direct Memory Access)operations.
 9. The method of claim 1, further comprising selecting, foreach of a plurality of locks, a different one of the plurality ofcompute nodes to host the lock. 10-20. (canceled)